Ontological mapping of data

ABSTRACT

Systems, methods, and non-transitory computer readable media are provided for mapping data based on an ontology of a platform. A data set may be obtained. Data within the data set may be for use by an operation platform based on an operation ontology. The operation ontology may define an operation data structure for the operation platform. The data may be shaped based on a target ontology. The target ontology may define a target data structure for a target platform. The data may be shaped such that the data is mapped to the target data structure.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/997,419, filed Jun. 4, 2018, which claims the benefit under 35 U.S.C.§ 119(e) of U.S. Provisional Application Ser. No. 62/671,941, filed May15, 2018, the content of which is incorporated by reference in itsentirety into the present disclosure.

FIELD OF THE INVENTION

This disclosure relates to approaches for mapping data based on anontology of a platform.

BACKGROUND

Under conventional approaches, data may be transformed for import into aplatform by applying the ontology of the platform onto the data. Forexample, tabular data may be transformed into object data for importinto an object-oriented platform by applying the ontology of theobject-oriented platform onto the tabular data. However, importation ofdata into a platform may restrict operations on the data toplatform-oriented operations. For example, importation of data into anobject-oriented platform may restrict operations on the data toobject-oriented operations.

SUMMARY

Various embodiments of the present disclosure may include systems,methods, and non-transitory computer readable media configured to mapdata based on an ontology of a platform. A data set may be obtained.Data within the data set may be organized for use by an operationplatform based on an operation ontology. The operation ontology maydefine an operation data structure for the operation platform. The datamay be shaped based on a target ontology. The target ontology may definea target data structure for a target platform. The data may be shapedsuch that the data is mapped to the target data structure.

In some embodiments, the operation platform may include a table-orientedplatform, and the operation ontology may include a table-orientedontology. The table-oriented ontology may define a table-oriented datastructure for the table-oriented platform. The target platform mayinclude an object-oriented platform, and the target ontology may includean object-oriented ontology. The object-oriented ontology may define anobject-oriented data structure for the object-oriented platform. Forexample, the object-oriented data structure may define objects and linksbetween the objects.

In some embodiments, organizing the data based on the table-orientedontology may include organizing the data into a single row or a singlecolumn based on the table-oriented ontology.

In some embodiments, organizing the data based on the table-orientedontology may enable the table-oriented platform to perform tabularoperations on the data. For example, the tabular operations on the datamay include a diff operation that determines changes between multipleversions of the data. The changes between the multiple versions of datamay be used to stage changes to the object data.

In some embodiments, shaping the data based on the object-orientedontology may include ordering the data within the single row or thesingle column or generating tables from the data based on theobject-oriented ontology. For example, generating the tables from thedata based on the object-oriented ontology may include generating tablesfor the objects and the links between the objects.

In some embodiments, the data may be mapped to the target data structuresuch that a tabular data of the table-oriented platform isrepresentative of an object data of the object-oriented platform. Thetabular data may be linked to the object data.

These and other features of the systems, methods, and non-transitorycomputer readable media disclosed herein, as well as the methods ofoperation and functions of the related elements of structure and thecombination of parts and economies of manufacture, will become moreapparent upon consideration of the following description and theappended claims with reference to the accompanying drawings, all ofwhich form a part of this specification, wherein like reference numeralsdesignate corresponding parts in the various figures. It is to beexpressly understood, however, that the drawings are for purposes ofillustration and description only and are not intended as a definitionof the limits of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain features of various embodiments of the present technology areset forth with particularity in the appended claims. A betterunderstanding of the features and advantages of the technology will beobtained by reference to the following detailed description that setsforth illustrative embodiments, in which the principles of the inventionare utilized, and the accompanying drawings of which:

FIG. 1 illustrates an example environment for mapping data based on anontology of a platform, in accordance with various embodiments.

FIG. 2 illustrates an example data set, in accordance with variousembodiments.

FIG. 3 illustrates example object data, in accordance with variousembodiments.

FIG. 4A illustrates example tabular data, in accordance with variousembodiments.

FIG. 4B illustrates example tabular data, in accordance with variousembodiments.

FIG. 4C illustrates example tabular data, in accordance with variousembodiments.

FIG. 5 illustrates an example data pipeline, in accordance with variousembodiments.

FIG. 6 illustrates a flowchart of an example method, in accordance withvarious embodiments.

FIG. 7 illustrates a block diagram of an example computer system inwhich any of the embodiments described herein may be implemented.

DETAILED DESCRIPTION

A claimed solution rooted in computer technology overcomes problemsspecifically arising in the realm of computer technology. In variousimplementations, a computing system is configured to obtain a data set.The data set may include a collection of information relating to anentity, such as a person or an organization, a document, an event, orother things. Data within the data set may be organized for use by anoperation platform based on an operation ontology. A platform may referto hardware, application, framework, browser, service, and/or otherplatform. The operation ontology may define an operation data structurefor the operation platform. The data may be shaped based on a targetontology. The target ontology may define a target data structure for atarget platform. The data may be shaped such that the data is mapped tothe target data structure. The organization and/or shaping of the datamay be performed at the beginning of a pipeline, at the end of thepipeline, or between the beginning and the end of the pipeline.

For example, the operation platform may include a table-orientedplatform and the operation ontology may include a table-orientedontology defining a table-oriented data structure for the table-orientedplatform. The target platform may include an object-oriented platformand the target ontology may include an object-oriented ontology definingan object-oriented data structure for the object-oriented platform. Thedata may mapped to the target data structure such that a tabular data ofthe table-oriented platform is representative of an object data of theobject-oriented platform. The tabular data may or may not be linked tothe object data. Linking of the tabular data to the object data mayprovide for synchronization of changes between the tabular data and theobject data.

Organizing the data based on the table-oriented ontology may includeorganizing the data into a single row or a single column based on thetable-oriented ontology. Shaping the data based on the object-orientedontology may include ordering the data within the single row or thesingle column, or generating tables from the data based on theobject-oriented ontology. For example, the object-oriented datastructure may define objects and links between the objects, andgenerating the tables from the data based on the object-orientedontology may include generating tables for the objects and the linksbetween the objects.

Organizing the data based on the table-oriented ontology may enable thetable-oriented platform to perform tabular operations on the data, suchas a search operation to find data, a select operation to select datafor use, a data manipulation operation to modify data, a diff operationto determine changes between multiple versions of data, a join operationto combine data, and/or a divide operation to separate data. Changesbetween multiple versions of data may be used to stage changes to anobject data of the object-oriented platform.

The approaches disclosed herein may facilitate ontological mapping ofdata, where data of one platform is mapped based on an ontology ofanother platform. Data may be organized based on an ontology of oneplatform and shaped based on an ontology of another platform. Forexample, rather than importing data from a table-oriented platform intoan object-oriented platform, which may restrict the type of operationsthat may be directly performed on the data to object operations, thedata of the table-oriented platform, which is organized based on atable-oriented ontology, may be shaped based on the object-orientedontology of the object-oriented platform. Such shaping of the data basedon the object-oriented ontology may map a tabular representation of thedata in the table-oriented platform to an object representation of thedata in the object-oriented platform. Such tabular representation of thedata may enable a user to use the table-oriented platform to directlyperform tabular operations on the data.

FIG. 1 illustrates an example environment 100 for mapping data based onan ontology of a platform, in accordance with various embodiments. Theexample environment 100 may include a computing system 102. Thecomputing system 102 may include one or more processors and memory. Theprocessor(s) may be configured to perform various operations byinterpreting machine-readable instructions stored in the memory. Theenvironment 100 may also include one or more datastores (not shown) thatis accessible to the computing system 102 (e.g., via one or morenetwork(s)). In some embodiments, the datastore(s) may include variousdatabases, application functionalities, application/data packages,and/or other data that are available for download, installation, and/orexecution.

In various embodiments, the computing system 102 may include a datastore112, a data set engine 114, an organization engine 116, a shape engine118, other engines, and/or other components. The datastore 112 mayinclude structured and/or unstructured sets of data that can bedivided/extracted for provision when needed by one or more components ofthe environment 100. The datastore 112 may include one or more datasetsof information. The datastore 112 may include one or more databases. Thedatastore 112 may include different data analysis modules thatfacilitate different data analysis tasks, patches for theapplications/systems, custom application/functionalities built forparticular application/systems, and/or other information to be used inthe environment 100. While the computing system 102 is shown in FIG. 1as a single entity, this is merely for ease of reference and is notmeant to be limiting. One or more components/functionalities of thecomputing system 100 described herein may be implemented in a singlecomputing device or multiple computing devices.

In various embodiments, the data set engine 114 may be configured toobtain one or more data sets. Obtaining a data set may includeaccessing, acquiring, analyzing, determining, examining, identifying,loading, locating, opening, receiving, retrieving, reviewing, storing,and/or otherwise obtaining the data set. For example, the data setengine 114 may search for and/or retrieve a data set relating to anentity. A data set may be obtained from one or more storage locations. Astorage location may refer to electronic storage located within thecomputing system 102 (e.g., integral and/or removable memory of thecomputing system 102), electronic storage coupled to the computingsystem 102, and/or electronic storage located remotely from thecomputing system 102 (e.g., electronic storage accessible to thecomputing system 102 through a network). A data set may be obtained fromone or more databases (e.g., stored within the datastore 112). A dataset may be stored within a single file or across multiple files. Forexample, A data set relating to an entity may have been ingested into adatabase as one or more objects, and the data set engine 114 mayretrieve the object(s) to obtain the data set.

A data set may include one or more collections of information relatingto an entity. An entity may refer to a thing with distinct existence,such as a person, an organization, a document, an event, and/or otherthings. For example, a data set may include one or more collections ofinformation relating to a person's name/identifier (e.g., driver licensenumber, login name, nickname, handle), physical address, email address,telephone number, date of birth, birthplace, account, activity, and/orother information relating to the person. Other types of data set arecontemplated.

Data within a data set may be organized according to one or moreschemas. For example, a data set may be obtained from a source that havethe data within the data set according to a source schema, with thesource schema specifying a format for how the data is structured. Asanother example, a data set may be obtained from multiple sources thathave the data within the data set according to one or more sourceschemas. Data may be obtained from multiple sources and collected into adata set. In some embodiments, a data set may be obtained by parsingdata obtained from one or more sources to identify and collect datarelevant to an entity. For example, a data set for an entity may beobtained by parsing one or more data objects for data that correspondsto and/or is associated with the entity. For instance, one or more dataobjects may be parsed for data that corresponds to and/or is associatedwith a person by looking for data that matches an identifier for theperson.

In various embodiments, the organization engine 116 may be configured toorganize one or more data within the data set for use by an operationplatform. A platform may refer to an environment in which applicationand/or software is executed. A platform may refer to hardware,application, framework, browser, service, and/or other platform. Anoperation platform may refer to a platform in which one or moreoperations on data may be performed. The organization engine 116 mayorganize one or more data within the data set to facilitate theoperation platform to perform one or more operations on the data. Theorganization engine 116 may organize the data based on an operationontology and/or other information. An ontology may refer to a set ofconcepts and/or categories for data objects that define their propertiesand/or relationships between them. An ontology may define types of dataused by a platform and/or how the data are stored for use by theplatform. For instance, an ontology may define object types, objectproperties, and/or relationships of objects that exist within theplatform. The operation ontology may define an operation data structurefor the operation platform. The operation ontology may define objecttypes, object properties, and/or relationships of objects that existwithin the operation platform.

For example, the operation platform may include a table-orientedplatform, and the operation ontology may include a table-orientedontology. A table-oriented platform may refer to an environment in whichapplication and/or software that use tabular data is executed. Thetable-oriented ontology may define a table-oriented data structure forthe table-oriented platform. For instance, the table-oriented ontologymay define how data is structured in a tabular form, such as in one ormore cells, one or more rows, one or more columns, and/or one or moretables. The table-oriented ontology may define how data in differentcells, rows, columns, and/or tables may be related to each other and/orassociated with an entity.

In some embodiments, organizing the data based on the table-orientedontology may include organizing the data into a single row or a singlecolumn based on the table-oriented ontology. For example, a data set mayinclude a collection of information relating to a person, such asproperties of the person, events associated with the person (e.g.,events attended by the person), accounts associated with the person(e.g., accounts used by the person, accounts in the person's name), theperson's first name, the person's last name, and/or other informationrelating to the person. The organization engine 116 may organize one ormore data within the data set based on the table-oriented ontology suchthat the properties of the person, the events associated with theperson, the accounts associated with the person, the person's firstname, the person's last name, and/or other information relating to theperson are organized into a single row or a single column within a tablebased on the table-oriented ontology. The data may be organized into asingle cell or multiple cells within the single row/column.

That is, the organization engine 116 may gather the relevant data for anentity, such as a person, into a single row or a single column. Suchorganization of data facilitates operations relating to the data for theentity because the relevant data is gathered together. Rather thanperforming operations on one or more data and bringing the modified datatogether using complicated join operations (which may be prone to errorthat results in wrong data/wrong type of data being joined together),the organization engine 116 may organize the data within the data setbased on the table-oriented ontology before operations are performed onthe data. In some embodiments, organizing the data based on thetable-oriented ontology may include performing one or more clean-upoperations on the data before and/or after the data is organized into asingle row or a single column. Other types of operation platform,operation ontology, and organization of data are contemplated.

In some embodiments, organizing the data based on the table-orientedontology may enable the table-oriented platform to perform tabularoperations on the data. Tabular operations may refer to operations ondata stored in tabular form. Tabular operations may use and/or modifydata stored within one or more cells, one or more rows, one or morecolumns, and/or one or more tables. Tabular operations may create and/orremove data within one or more cells, one or more rows, one or morecolumns, and/or one or more tables. Tabular operations may use and/ormodify (e.g., create, change, remove) one or more cells, one or morerows, one or more columns, and/or one or more tables. Tabular operationsmay include data operations that take advantage of data stored intabular form. For example, tabular operations may include a searchoperation to find data, a select operation to select data for use, adata manipulation operation to modify data, a diff operation todetermine changes between multiple versions of data, a join operation tocombine data, a divide operation to separate data and/or other tabularoperations. In some embodiments, changes between multiple versions ofdata (e.g., as determined by a diff operation) may be used to stagechanges to the data and/or corresponding data object(s), such as anobject data of an object-oriented platform.

In various embodiments, the shape engine 118 may be configured to shapethe data based on a target ontology and/or other information. A targetontology may define a target data structure for a target platform. Atarget ontology may define object types, object properties, and/orrelationships of objects that exist within a target platform. A targetplatform may refer to a platform in which the data is to be providedand/or to a platform to which the data is to be mapped. The shape engine118 may shaped the data based on the target ontology such that the datais mapped to the target data structure.

For example, the target platform may include an object-orientedplatform, and the target ontology may include an object-orientedontology. An object-oriented platform may refer to an environment inwhich application and/or software that use object data is executed. Theobject-oriented ontology may define an object-oriented data structurefor the object-oriented platform. For instance, the object-orientedontology may define how data is structured in an object form, such aswithin one or more JSON objects. The object-oriented ontology may defineobjects and links between objects. The object-oriented ontology maydefine properties of objects and/or links between objects. For example,the shape engine 118 may shape tabular data (e.g., organized by theorganization engine 116) based on the object-oriented ontology such thatthe tabular data of a table-oriented platform is representative of anobject data of the object-oriented platform. Other types of targetplatform, target ontology, and shaping of data are contemplated.

For example, an object-oriented ontology may define one or more types ofobjects (object data) and how the objects are linked to (e.g., relatedto, associated with) other objects. The object-oriented ontology maydefine properties of objects/different types of objects and/orproperties of links/different types of links between objects. Theobject-oriented ontology may define the kinds of things that may berepresented in the object-oriented platform, and may provide structurefor object. Different objects/object types may be derived from, forexample, entity types (e.g., person, organization, document, event). Theobject-oriented ontology may define numbers and/or types of propertiesof the object, such as identifying data, characteristics data, temporaldata, geospatial data, and/or other data associated with theobject/entity. The object-oriented ontology may define what types oflinks are allowed to be made and/or to exist with the object.

For instance, an object-oriented ontology may define an object data(person object) used to store data about a person, and may include dataproperties for storing name, address, occupation, e-mail address, phonenumber, and/or other information relating to the person. Theobject-oriented ontology may permit the person object to be linked toother person objects (e.g., friends, associates), linked to organizationobjects (e.g., organizations to which the person belongs), linked toevent objects (e.g., events attended or invited to), linked to documentobjects (e.g., authored/used by the person), linked to account objects(e.g., opened/used by the person), and/or other objects. Theobject-oriented ontology may define the structure of data stored as anobject property, such as data fields and/or type of data associated withthe data fields. The object-oriented ontology define links that mayexist between two objects, such as whether two objects/two object typesmay be linked, whether the links are directional or symmetric, and/orother properties of the links. The shape engine 118 may shape the databased on the type(s) of objects defined by the object-oriented ontology,links between objects, properties of the objects, and/or properties ofthe links between objects.

In some embodiments, shaping the data based on the object-orientedontology may include ordering the data within the single row or thesingle column based on the object-oriented ontology. For example, datarelating to a person may be organized (e.g., by the organization engine116) into a single row or a single column within a table based on thetable-oriented ontology. For instance, a single row or a single columnwithin a table may contain, in order, the properties of the person, theevents associated with the person, the accounts associated with theperson, the person's first name, the person's last name, and/or otherinformation relating to the person. The data may be organized into asingle cell or multiple cells within the single row/column. The shapeengine 118 may shape the data by ordering the data within the singlerow/column to match the object-oriented data structure of theobject-oriented ontology. For example, the data within the singlerow/column may be ordered such that the single row/column may contain,in order, the person's last name, the person's first name, theproperties of the person, the accounts associated with the person, theevents associated with the person, and/or other information relating tothe person. In some embodiments, the shape engine 118 may shape the databy splitting data contained in one cell among multiple cells. Forexample, the shape engine 118 may generate columns or rows for differenttypes of data. For instance, the shape engine 118 may generate a lastname column, a first name column, one or more property columns, one ormore account columns, one or more event columns, and/or other columnsand place the data within relevant column(s).

In some embodiments, shaping the data based on the object-orientedontology may include generating tables from the data based on theobject-oriented ontology. The object-oriented data structure may defineobjects and links between the objects, and the shape engine 118 maygenerate tables for the objects and the links between the objects. Forexample, the shape engine 118 may generate tables for different types ofobjects and generate one or more tables for links between the objects.For example, the shape engine 118 may generate a person table (listingdifferent persons), a property table (listing different properties ofpersons), an account table (listing different accounts), an event table(listing different events), a link table (listing relationships betweenentries in different tables, such as a relationship pair), and/or othertables, and place the data within relevant table(s). In someembodiments, multiple types of data may be placed within a single table.For instance, the person table and the property table may be merged intoa single person table such that the person table lists both differentpersons (e.g., separated into different rows) and different propertiesof the persons (e.g., separated into different columns).

The organization and shaping of data disclosed herein may result in dataof an operation platform being structured based on a target ontology.That is, the data may be integrated for use by an operation platformwhile respecting the data structure of a target platform. For example,data within a data set may be organized into one or more rows, one ormore columns, and/or one or more tables based on a table-orientedontology of a table-oriented platform (operation platform) while thestructure of the row(s), column(s), and/or table(s) and/or the placementof data within the row(s), column(s), and/or table(s) may be determinedbased on an object-oriented ontology of an object-oriented platform(target platform). The data may be stored in row(s), column(s), and/ortable(s) based on how the data is stored in an object-representation(e.g., JSON representation) of the object-oriented platform. That is,the data may be mapped to the object-oriented data structure (targetdata structure) such that a tabular data of the table-oriented platform(operation platform) is representative of an object data of theobject-oriented platform (target platform).

The data of the operation platform may be used to perform one or moreoperations on the data. For example, the tabular data of thetable-oriented platform may be used to perform one or more tabularoperations of the data. Rather than importing the data into the targetplatform (e.g., object-oriented platform), the operation platform (e.g.,table-oriented platform) may be used to perform operations on the data.Such may provide for more efficient (e.g., less computationallyintensive, faster) operations on the data. For example, anobject-oriented platform may not be efficient when running operationssuch as searches or collecting statistical information from datacontained within object data. A table-oriented platform may be moresuited to perform searches, collecting statically information, and/orother operations that are more suited to be performed on data in tabularform. Rather than importing the data into an object-oriented platform asan object data (and performing costly operations by the object-orientedplatform or importing the object data back into tabular form), arepresenting of the object data may be stored as tabular data for thetable-oriented platform to perform tabular operations. For example, atabular representation of a JSON object may be stored for tabularoperations to be run by the table-oriented platform. The tabular datamay be kept as a representation of the object data in the table-orientedplatform and/or imported into the object-oriented platform for objectoperations.

The tabular data of the table-oriented platform (operation platform) maybe an equal but separate copy of the data within the object data of theobject-oriented platform (target platform). The tabular data of thetable-oriented platform may or may not be linked to the object data ofthe object-oriented platform. Linking of the tabular data to the objectdata may provide for synchronization of changes between the tabular dataand the object data. That is, based on changes to the tabular data ofthe table-oriented platform, changes to the linked object data may bepushed to the object-oriented platform. Based on changes to the objectdata of the object-oriented platform, changes to the linked tabular datamay be pushed to the table-oriented platform.

The organization and shaping of data disclosed herein may result in moreefficient staging of data for the target platform. For example,generating a tabular data that is representative of an object data of anobject-oriented platform (target platform) may allow for more efficientstaging of changes to the object data. Importation of data into anobject-oriented platform may require staging all data into a stagingarea, comparing the data in the staging area to the object data alreadypresent in the object-oriented platform, and then importing thedifference between the staged data and the object data in theobject-oriented platform to the object-oriented platform. Staging datain the staging area and performing a comparison of object data may beresource intensive (e.g., processing power, processing time, memoryusage).

The table-oriented platform may be able to more efficiently performand/or otherwise streamline one or more steps for staging data. Forexample, rather than determining changes to the object data for stagingbased on comparison of multiple versions of the object data, a diffoperation may be performed on the tabular data to determine changesbetween multiple versions of the data. That is, changes between multipleversions of the data determined using the tabular data representation ofthe object data may be used to stage changes to the object data of theobject-oriented platform. The comparison of different versions of thetabular data (e.g., a diff operation) may be performed more efficientlyby the table-oriented platform than the object-oriented platform, and/ormay be performed in a distributed fashion by the table-orientedplatform. As another example, changes to the tabular data may be storedby the table-oriented platform for use in determining how the tabulardata has changed and in determining what data should be staged forimportation into the object-oriented platform. The table-orientedplatform may track (e.g., using version control) how data has beenchanged (e.g., addition of data, modification of data, removal of data)since prior importation into the object-oriented platform. By using thetabular data of the table-oriented platform, only changes to the objectdata (e.g., addition of data, modification of data, removal of data) inthe object-oriented platform may be staged within the staging area andmay be imported into the object-oriented platform without a comparisonwith the object data in the object-oriented platform.

The organization and shaping of data disclosed herein may be flexible interms of where and when they are performed. For example, organizationand/or shaping of data into a tabular data that is representative of anobject data may be performed at the beginning of a pipeline forprocessing data, at the end of the pipeline for processing data, orbetween the beginning and the end of the pipeline for processing data.Once the tabular data representative of the object data is generated,other tabular operations may be performed on the data as part of and/orin addition to the pipeline for processing data.

FIG. 2 illustrates an example data set 200, in accordance with variousembodiments. The data set 200 may include one or more collections ofinformation relating to an entity, such as a person, an organization, adocument, an event, and/or other things. For example, the data set 200may include one or more collections of information relating to a person,such as information relating to (e.g., identifying, describing)properties of the person (a property A 202, a property B 204), eventsassociated with the person (an event A 206, an event B 208), accountsassociated with the person (an account A 210, an account B 212), theperson's first name (a first name 214), the person's last name (a lastname 216), and/or other information relating to the person. The datawithin the data set 200 by obtained from a single source or multiplesources. For example, the data within the data set 200 may be obtainedfrom a single object data of an object-oriented platform or obtainedfrom multiple sources and collected into the data set 200. In someembodiments, the data set 200 may be obtained by parsing data obtainedfrom one or more sources to identify and collect data relevant to theperson. For example, the data set 200 may be obtained by parsing one ormore data objects for data that corresponds to and/or is associated withthe person. For instance, one or more data objects may be parsed fordata that corresponds to and/or is associated with the person by lookingfor data that matches an identifier for the person.

FIG. 3 illustrates example object data 300, 306, 308, 310, 312, inaccordance with various embodiments. The structure of the object data300, 306, 308, 310, 312 may be defined by an object-oriented ontology.That is, the object-oriented ontology may define how data is structuredin an object form, such as within one or more of the object data 300,306, 308, 310, 312. The object-oriented ontology may define the objectdata 300, 306, 308, 310, 312 and links 322, 324, 326, 328 between theobject data 300, 306, 308, 310, 312. The links 322, 324, 326, 328 mayrepresent one or more relationships between the entity object data 300and the other object data 310, 312, 306, 308. The object-orientedontology may define properties of object data 300, 306, 308, 310, 312and/or links 322, 324, 326, 328 between object data. For example, theobject-oriented ontology may define the entity object data 300 asincluding data for a last name 316, a first name 314, a property A 302,and a property B 304. The properties of the links 322, 324, 326, 328 maydefine the directionality of the links 322, 324, 326, 328.

FIG. 4A illustrates example tabular data 400, 420, in accordance withvarious embodiments. The tabular data 400, 420 may include data that isorganized based on an ontology of a platform. For example, the tabulardata 400, 420 may include one or more data within the data set 200 thatis organized according to a table-oriented ontology of a table-orientedplatform. For instance, the table-oriented ontology may define atable-oriented data structure for the table-oriented platform. Thetable-oriented ontology may define how data is structured in a tabularform, such as in one or more cells, one or more rows, one or morecolumns, and/or one or more tables. The table-oriented ontology maydefine how data in different cells, rows, columns, and/or tables may berelated to each other and/or associated with an entity.

For example, one or more data within a data set may be organized into asingle row as shown in the tabular data 400, 420. The tabular data 400may include, in order, data for a property A 402, and a property B 404,an event A 406, an event B 408, an account A 410, an account B 412, afirst name 414, a last name 416 of a person. The data within the tabulardata 400 may be included within a single cell of a table. The tabulardata 420 may include, in order, data for a property A 422, and aproperty B 424, an event A 426, an event B 428, an account A 430, anaccount B 432, a first name 434, a last name 434 of a person. The datawithin the tabular data 402 may be included within multiple cells of arow of a table. For example, different data may be included withindifferent columns within a row of a table.

FIG. 4B illustrates example tabular data 440, 460, in accordance withvarious embodiments. The tabular data 440, 460 may include data that isshaped based on an ontology of a platform. For example, the tabular data440, 460 may include one or more data within the data set 200, thetabular data 400, and/or the tabular data 420 that is shaped accordingto an object-oriented ontology of an object-oriented platform. Forinstance, the object-oriented ontology may define an object-orienteddata structure for the object-oriented platform, such as shown in FIG.3.

For example, one or more data within the data set 200 and/or the tabulardata 400, 420 may be shaped into a single row as shown in the tabulardata 440, 460. The tabular data 440 may include, in order, data for alast name 456, a first name 454, a property A 442, a property B 444, anaccount A 450, an account B 452, an event A 446, and an event B 448 of aperson. The tabular data 460 may include, in order, data for a last name476, a first name 474, a property A 462, a property B 464, an account A470, an account B 472, an event A 466, and an event B 468 of a person.For example, different data may be included within different columnswithin a row of a table. The data within the tabular data 440, 460 maybe shaped to match the object-oriented data structure of theobject-oriented ontology, such as shown in FIG. 3. The data may beincluded within a single cell of a table, such as shown in the tabulardata 440. The data may be included within a multiple cells of a row of atable, such as shown in the tabular data 460.

FIG. 4C illustrates example tabular data 480, in accordance with variousembodiments. The tabular data 480 may include data that is shaped basedon an ontology of a platform. For example, the tabular data 480 mayinclude one or more data within the data set 200, the tabular data 400,and/or the tabular data 420 that is shaped according to anobject-oriented ontology of an object-oriented platform. For instance,the object-oriented ontology may define an object-oriented datastructure for the object-oriented platform, such as shown in FIG. 3.

For example, one or more data within the data set 200 and/or the tabulardata 400, 420 may be shaped into a person table 482, an account table484, an event table 486, and a link table 488. The person table 482 maylist persons within the data set 200 and/or the tabular data 400, 420,the account table 484 may list accounts within the data set 200 and/orthe tabular data 400, 420, the event table 486 may list events withinthe data set 200 and/or the tabular data 400, 420, and the link table488 may list relationships between entries in different tables 482, 484,486, such as different relationship pairs. In some embodiments, multipletypes of data may be placed within a single table. For instance, theperson table 482 may lists both persons (e.g., separated into differentrows) and properties of the persons (e.g., separated into differentcolumns).

FIG. 5 illustrates an example data pipeline 500, in accordance withvarious embodiments. A data pipeline may include multiple dataprocessing elements in a sequence. For example, the data pipeline 500may include data processing elements 502, 504, 506, 508, 510 in asequence. A data pipeline may include a linear pipeline, such as shownin the data pipeline 500, or a branching pipeline. The organization ofdata based on an operation ontology and shaping of data based on atarget ontology may be flexible in terms of where these data processingelements are placed within a data pipeline. For example, organizationand/or shaping of data into a tabular data that is representative of anobject data may be performed at the beginning of the data pipeline 500(represented by the data processing element 502), at the end of the datapipeline 500 (represented by the data processing element 510), orbetween the beginning and the end of the data pipeline f500 (representedby one or more of the data processing elements 504, 506, 508). Once thetabular data representative of the object data is generated, otheroperations may be performed on the data as part of and/or in addition tothe data pipeline 500.

FIG. 6 illustrates a flowchart of an example method 600, according tovarious embodiments of the present disclosure. The method 600 may beimplemented in various environments including, for example, theenvironment 100 of FIG. 1. The operations of method 600 presented beloware intended to be illustrative. Depending on the implementation, theexample method 600 may include additional, fewer, or alternative stepsperformed in various orders or in parallel. The example method 600 maybe implemented in various computing systems or devices including one ormore processors.

At block 602, a data set may be obtained. At block 604, data within thedata set may be organized for use by an operation platform based on anoperation ontology. The operation ontology may define an operation datastructure for the operation platform. At block 606, the data may beshaped based on a target ontology. The target ontology may define atarget data structure for a target platform. The data may be shaped suchthat the data is mapped to the target data structure.

Hardware Implementation

The techniques described herein are implemented by one or morespecial-purpose computing devices. The special-purpose computing devicesmay be hard-wired to perform the techniques, or may include circuitry ordigital electronic devices such as one or more application-specificintegrated circuits (ASICs) or field programmable gate arrays (FPGAs)that are persistently programmed to perform the techniques, or mayinclude one or more hardware processors programmed to perform thetechniques pursuant to program instructions in firmware, memory, otherstorage, or a combination. Such special-purpose computing devices mayalso combine custom hard-wired logic, ASICs, or FPGAs with customprogramming to accomplish the techniques. The special-purpose computingdevices may be desktop computer systems, server computer systems,portable computer systems, handheld devices, networking devices or anyother device or combination of devices that incorporate hard-wiredand/or program logic to implement the techniques.

Computing device(s) are generally controlled and coordinated byoperating system software, such as iOS, Android, Chrome OS, Windows XP,Windows Vista, Windows 7, Windows 8, Windows Server, Windows CE, Unix,Linux, SunOS, Solaris, iOS, Blackberry OS, VxWorks, or other compatibleoperating systems. In other embodiments, the computing device may becontrolled by a proprietary operating system. Conventional operatingsystems control and schedule computer processes for execution, performmemory management, provide file system, networking, I/O services, andprovide a user interface functionality, such as a graphical userinterface (“GUI”), among other things.

FIG. 7 is a block diagram that illustrates a computer system 700 uponwhich any of the embodiments described herein may be implemented. Thecomputer system 700 includes a bus 702 or other communication mechanismfor communicating information, one or more hardware processors 704coupled with bus 702 for processing information. Hardware processor(s)704 may be, for example, one or more general purpose microprocessors.

The computer system 700 also includes a main memory 706, such as arandom access memory (RAM), cache and/or other dynamic storage devices,coupled to bus 702 for storing information and instructions to beexecuted by processor 704. Main memory 706 also may be used for storingtemporary variables or other intermediate information during executionof instructions to be executed by processor 704. Such instructions, whenstored in storage media accessible to processor 704, render computersystem 700 into a special-purpose machine that is customized to performthe operations specified in the instructions.

The computer system 700 further includes a read only memory (ROM) 708 orother static storage device coupled to bus 702 for storing staticinformation and instructions for processor 704. A storage device 710,such as a magnetic disk, optical disk, or USB thumb drive (Flash drive),etc., is provided and coupled to bus 702 for storing information andinstructions.

The computer system 700 may be coupled via bus 702 to a display 712,such as a cathode ray tube (CRT) or LCD display (or touch screen), fordisplaying information to a computer user. An input device 714,including alphanumeric and other keys, is coupled to bus 702 forcommunicating information and command selections to processor 704.Another type of user input device is cursor control 716, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 704 and for controllingcursor movement on display 712. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Insome embodiments, the same direction information and command selectionsas cursor control may be implemented via receiving touches on a touchscreen without a cursor.

The computing system 700 may include a user interface module toimplement a GUI that may be stored in a mass storage device asexecutable software codes that are executed by the computing device(s).This and other modules may include, by way of example, components, suchas software components, object-oriented software components, classcomponents and task components, processes, functions, attributes,procedures, subroutines, segments of program code, drivers, firmware,microcode, circuitry, data, databases, data structures, tables, arrays,and variables.

In general, the word “module,” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,possibly having entry and exit points, written in a programminglanguage, such as, for example, Java, C or C++. A software module may becompiled and linked into an executable program, installed in a dynamiclink library, or may be written in an interpreted programming languagesuch as, for example, BASIC, Perl, or Python. It will be appreciatedthat software modules may be callable from other modules or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules configured for execution on computingdevices may be provided on a computer readable medium, such as a compactdisc, digital video disc, flash drive, magnetic disc, or any othertangible medium, or as a digital download (and may be originally storedin a compressed or installable format that requires installation,decompression or decryption prior to execution). Such software code maybe stored, partially or fully, on a memory device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules may be comprised of connectedlogic units, such as gates and flip-flops, and/or may be comprised ofprogrammable units, such as programmable gate arrays or processors. Themodules or computing device functionality described herein arepreferably implemented as software modules, but may be represented inhardware or firmware. Generally, the modules described herein refer tological modules that may be combined with other modules or divided intosub-modules despite their physical organization or storage.

The computer system 700 may implement the techniques described hereinusing customized hard-wired logic, one or more ASICs or FPGAs, firmwareand/or program logic which in combination with the computer systemcauses or programs computer system 700 to be a special-purpose machine.According to one embodiment, the techniques herein are performed bycomputer system 700 in response to processor(s) 704 executing one ormore sequences of one or more instructions contained in main memory 706.Such instructions may be read into main memory 706 from another storagemedium, such as storage device 710. Execution of the sequences ofinstructions contained in main memory 706 causes processor(s) 704 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “non-transitory media,” and similar terms, as used hereinrefers to any media that store data and/or instructions that cause amachine to operate in a specific fashion. Such non-transitory media maycomprise non-volatile media and/or volatile media. Non-volatile mediaincludes, for example, optical or magnetic disks, such as storage device710. Volatile media includes dynamic memory, such as main memory 706.Common forms of non-transitory media include, for example, a floppydisk, a flexible disk, hard disk, solid state drive, magnetic tape, orany other magnetic data storage medium, a CD-ROM, any other optical datastorage medium, any physical medium with patterns of holes, a RAM, aPROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge, and networked versions of the same.

Non-transitory media is distinct from but may be used in conjunctionwith transmission media. Transmission media participates in transferringinformation between non-transitory media. For example, transmissionmedia includes coaxial cables, copper wire and fiber optics, includingthe wires that comprise bus 702. Transmission media can also take theform of acoustic or light waves, such as those generated duringradio-wave and infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 704 for execution. For example,the instructions may initially be carried on a magnetic disk or solidstate drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 700 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 702. Bus 702 carries the data tomain memory 706, from which processor 704 retrieves and executes theinstructions. The instructions received by main memory 706 may retrievesand executes the instructions. The instructions received by main memory706 may optionally be stored on storage device 710 either before orafter execution by processor 704.

The computer system 700 also includes a communication interface 718coupled to bus 702. Communication interface 718 provides a two-way datacommunication coupling to one or more network links that are connectedto one or more local networks. For example, communication interface 718may be an integrated services digital network (ISDN) card, cable modem,satellite modem, or a modem to provide a data communication connectionto a corresponding type of telephone line. As another example,communication interface 718 may be a local area network (LAN) card toprovide a data communication connection to a compatible LAN (or WANcomponent to communicated with a WAN). Wireless links may also beimplemented. In any such implementation, communication interface 718sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

A network link typically provides data communication through one or morenetworks to other data devices. For example, a network link may providea connection through local network to a host computer or to dataequipment operated by an Internet Service Provider (ISP). The ISP inturn provides data communication services through the world wide packetdata communication network now commonly referred to as the “Internet”.Local network and Internet both use electrical, electromagnetic oroptical signals that carry digital data streams. The signals through thevarious networks and the signals on network link and throughcommunication interface 718, which carry the digital data to and fromcomputer system 700, are example forms of transmission media.

The computer system 700 can send messages and receive data, includingprogram code, through the network(s), network link and communicationinterface 718. In the Internet example, a server might transmit arequested code for an application program through the Internet, the ISP,the local network and the communication interface 718.

The received code may be executed by processor 704 as it is received,and/or stored in storage device 710, or other non-volatile storage forlater execution.

Each of the processes, methods, and algorithms described in thepreceding sections may be embodied in, and fully or partially automatedby, code modules executed by one or more computer systems or computerprocessors comprising computer hardware. The processes and algorithmsmay be implemented partially or wholly in application-specificcircuitry.

The various features and processes described above may be usedindependently of one another, or may be combined in various ways. Allpossible combinations and sub-combinations are intended to fall withinthe scope of this disclosure. In addition, certain method or processblocks may be omitted in some implementations. The methods and processesdescribed herein are also not limited to any particular sequence, andthe blocks or states relating thereto can be performed in othersequences that are appropriate. For example, described blocks or statesmay be performed in an order other than that specifically disclosed, ormultiple blocks or states may be combined in a single block or state.The example blocks or states may be performed in serial, in parallel, orin some other manner. Blocks or states may be added to or removed fromthe disclosed example embodiments. The example systems and componentsdescribed herein may be configured differently than described. Forexample, elements may be added to, removed from, or rearranged comparedto the disclosed example embodiments.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Any process descriptions, elements, or blocks in the flow diagramsdescribed herein and/or depicted in the attached figures should beunderstood as potentially representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or steps in the process. Alternateimplementations are included within the scope of the embodimentsdescribed herein in which elements or functions may be deleted, executedout of order from that shown or discussed, including substantiallyconcurrently or in reverse order, depending on the functionalityinvolved, as would be understood by those skilled in the art.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other acceptable examples. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure. The foregoing description details certainembodiments of the invention. It will be appreciated, however, that nomatter how detailed the foregoing appears in text, the invention can bepracticed in many ways. As is also stated above, it should be noted thatthe use of particular terminology when describing certain features oraspects of the invention should not be taken to imply that theterminology is being re-defined herein to be restricted to including anyspecific characteristics of the features or aspects of the inventionwith which that terminology is associated. The scope of the inventionshould therefore be construed in accordance with the appended claims andany equivalents thereof.

Engines, Components, and Logic

Certain embodiments are described herein as including logic or a numberof components, engines, or mechanisms. Engines may constitute eithersoftware engines (e.g., code embodied on a machine-readable medium) orhardware engines. A “hardware engine” is a tangible unit capable ofperforming certain operations and may be configured or arranged in acertain physical manner. In various example embodiments, one or morecomputer systems (e.g., a standalone computer system, a client computersystem, or a server computer system) or one or more hardware engines ofa computer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware engine that operates to perform certain operations asdescribed herein.

In some embodiments, a hardware engine may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware engine may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware engine may be a special-purpose processor, such as aField-Programmable Gate Array (FPGA) or an Application SpecificIntegrated Circuit (ASIC). A hardware engine may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardware enginemay include software executed by a general-purpose processor or otherprogrammable processor. Once configured by such software, hardwareengines become specific machines (or specific components of a machine)uniquely tailored to perform the configured functions and are no longergeneral-purpose processors. It will be appreciated that the decision toimplement a hardware engine mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the phrase “hardware engine” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented engine” refers to a hardware engine. Consideringembodiments in which hardware engines are temporarily configured (e.g.,programmed), each of the hardware engines need not be configured orinstantiated at any one instance in time. For example, where a hardwareengine comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware engines) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware engine at one instance oftime and to constitute a different hardware engine at a differentinstance of time.

Hardware engines can provide information to, and receive informationfrom, other hardware engines. Accordingly, the described hardwareengines may be regarded as being communicatively coupled. Where multiplehardware engines exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware engines. In embodiments inwhich multiple hardware engines are configured or instantiated atdifferent times, communications between such hardware engines may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware engines have access.For example, one hardware engine may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware engine may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware engines may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented enginesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented engine” refers to ahardware engine implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented engines. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented engines may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented engines may be distributed across a number ofgeographic locations.

Language

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the subject matter has been described withreference to specific example embodiments, various modifications andchanges may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the subject matter may be referred to herein, individually orcollectively, by the term “invention” merely for convenience and withoutintending to voluntarily limit the scope of this application to anysingle disclosure or concept if more than one is, in fact, disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

It will be appreciated that an “engine,” “system,” “data store,” and/or“database” may comprise software, hardware, firmware, and/or circuitry.In one example, one or more software programs comprising instructionscapable of being executable by a processor may perform one or more ofthe functions of the engines, data stores, databases, or systemsdescribed herein. In another example, circuitry may perform the same orsimilar functions. Alternative embodiments may comprise more, less, orfunctionally equivalent engines, systems, data stores, or databases, andstill be within the scope of present embodiments. For example, thefunctionality of the various systems, engines, data stores, and/ordatabases may be combined or divided differently.

The data stores described herein may be any suitable structure (e.g., anactive database, a relational database, a self-referential database, atable, a matrix, an array, a flat file, a documented-oriented storagesystem, a non-relational No-SQL system, and the like), and may becloud-based or otherwise.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, engines, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Although the invention has been described in detail for the purpose ofillustration based on what is currently considered to be the mostpractical and preferred implementations, it is to be understood thatsuch detail is solely for that purpose and that the invention is notlimited to the disclosed implementations, but, on the contrary, isintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the appended claims. For example, it isto be understood that the present invention contemplates that, to theextent possible, one or more features of any embodiment can be combinedwith one or more features of any other embodiment.

1. A system comprising: one or more processors; and a memory storinginstructions that, when executed by the one or more processors, causethe system to perform: obtaining a data set; organizing the data setaccording to a source platform compatible with a source ontology;ordering the organized data set according to a target platformcompatible with a target ontology; importing the ordered and organizeddata set into the target platform; linking the imported data set tocorresponding data within the target platform to synchronizemodifications between the imported data set and the data within thetarget platform; receiving a first modification to a first portion ofthe imported data set; modifying a corresponding first portion of thedata within the target platform according to the first modification;receiving a second modification to a second portion of the data withinthe target platform; and modifying a corresponding second portion of theimported data set according to the second modification.
 2. The system ofclaim 1, wherein the instructions further cause the system to perform:modifying a portion of the organized data set in the source platform tobe synchronized with the modified corresponding second portion of theimported data set.
 3. The system of claim 1, wherein the ordering of theorganized data set comprises generating a first table enumeratingidentities of entities within the organized data set and a second tableenumerating relationships among the entities.
 4. The system of claim 3,wherein the ordering of the organized data set comprises generating athird table enumerating properties of the entities and a fourth tableenumerating relationships among the properties.
 5. The system of claim4, wherein the ordering of the organized data set comprises merging thefirst table with the third table.
 6. The system of claim 1, wherein theordering of the organized data set maintains the organization of thedata set according to the source platform.
 7. The system of claim 1,wherein the instructions further cause the system to perform: modifyinga portion of the organized data set in the source platform to besynchronized with the modified corresponding second portion of theimported data set, to generate a modified organized data set; andperforming, by the source platform, a search operation and a joinoperation on the modified organized data set following the modifying ofthe portion of the organized data set.
 8. The system of claim 1, whereinthe receiving of the first modification comprises an addition, adeletion, or a change of data in the imported data set.
 9. The system ofclaim 1, wherein the dataset comprises geospatial data.
 10. A methodimplemented by a computing system including one or more processors andstorage media storing machine-readable instructions, wherein the methodis performed using the one or more processors, the method comprising:obtaining a data set; organizing the data set according to a sourceplatform compatible with a source ontology; ordering the organized dataset according to a target platform compatible with a target ontology;importing the ordered and organized data set into the target platform;linking the imported data set to corresponding data within the targetplatform to synchronize modifications between the imported data set andthe data within the target platform; receiving a first modification to afirst portion of the imported data set; modifying a corresponding firstportion of the data within the target platform according to the firstmodification; receiving a second modification to a second portion of thedata within the target platform; and modifying a corresponding secondportion of the imported data set according to the second modification.11. The method of claim 10, further comprising: modifying a portion ofthe organized data set in the source platform to be synchronized withthe modified corresponding second portion of the imported data set. 12.The method of claim 10, wherein the ordering of the organized data setcomprises generating a first table enumerating identities of entitieswithin the organized data set and a second table enumeratingrelationships among the entities.
 13. The method of claim 12, whereinthe ordering of the organized data set comprises generating a thirdtable enumerating properties of the entities and a fourth tableenumerating relationships among the properties.
 14. The method of claim13, wherein the ordering of the organized data set comprises merging thefirst table with the third table.
 15. The method of claim 10, whereinthe ordering of the organized data set maintains the organization of thedata set according to the source platform.
 16. The method of claim 10,further comprising: modifying a portion of the organized data set in thesource platform to be synchronized with the modified correspondingsecond portion of the imported data set, to generate a modifiedorganized data set; and performing, by the source platform, a searchoperation and a join operation on the modified organized data setfollowing the modifying of the portion of the organized data set. 17.The method of claim 10, wherein the receiving of the first modificationcomprises an addition, a deletion, or a change of data in the importeddata set.
 18. A non-transitory computer readable medium comprisinginstructions that, when executed, cause one or more processors toperform: obtaining a data set; organizing the data set according to asource platform compatible with a source ontology; ordering theorganized data set according to a target platform compatible with atarget ontology; importing the ordered and organized data set into thetarget platform; linking the imported data set to corresponding datawithin the target platform to synchronize modifications between theimported data set and the data within the target platform; receiving afirst modification to a first portion of the imported data set;modifying a corresponding first portion of the data within the targetplatform according to the first modification; receiving a secondmodification to a second portion of the data within the target platform;and modifying a corresponding second portion of the imported data setaccording to the second modification.
 19. The non-transitory computerreadable medium of claim 18, wherein the instructions further cause theone or more processors to perform: modifying a portion of the organizeddata set in the source platform to be synchronized with the modifiedcorresponding second portion of the imported data set.
 20. Thenon-transitory computer readable medium of claim 18, wherein theordering of the organized data set comprises generating a first tableenumerating identities of entities within the organized data set and asecond table enumerating relationships among the entities.